跨麻醉剂ECoG状态解码在决策阈值处失败，而非表征层面
Cross-Anesthetic ECoG State Decoding Fails at the Decision Threshold, Not the Representation
- Zhejiang University of Technology（浙江工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究区分麻醉状态解码的表征与决策阈值，发现跨麻醉剂时表征可迁移，氯胺酮的失效源于决策阈值，锚定受试者基线的阈值可修复氯胺酮的解码性能。
AI中文摘要：
从皮层活动解码麻醉状态的解码器在不同药物类别间均失效，尤以氯胺酮最为显著，但报告的准确率无法说明是神经表征失效还是仅决策阈值失效；我们在带有真实标签的受控准备实验中区分了这两种情况。我们对小鼠的皮层电图（250 Hz带限局部场电位，采样率1875 Hz）进行清醒与麻醉状态的解码，采用留一麻醉剂法评估，涉及五种机制不同的麻醉剂（异氟烷、右美托咪定、氯胺酮、丙泊酚和咪达唑仑），比较了空间盲带功率解码器、协方差/黎曼表征以及黎曼域适应，所有统计量均为会话水平，氯胺酮折采用小鼠水平聚类自举法。表征可迁移：带功率在每个保留药物上的会话AUROC至少为0.96，包括氯胺酮（0.980，聚类置信区间0.821至1.000）。失败局限于阈值：三种表征下氯胺酮的排序近乎不变，但其平衡准确率从随机水平大幅波动，置换检验对排序显著（p=0.0025），但对固定阈值准确率不显著（p=0.3795）。黎曼域适应总体效果为负。锚定受试者自身诱导前基线的无标签因果阈值可修复氯胺酮（平衡准确率0.50至0.85），且优于域适应。由于氯胺酮测试会话来自同时贡献训练药物的三只小鼠，这是受试者内跨药物迁移；我们不主张跨受试者的群体水平迁移。在跨药物状态解码中，可操作的失效是校准问题，而非表征问题。
英文摘要：
Decoders of anesthetic state from cortical activity fail across drug classes, most notoriously ketamine, but reported accuracy cannot say whether the neural representation or only the decision threshold has failed; we separate the two in a controlled preparation with ground-truth labels. We decoded awake versus anesthetized from mouse electrocorticography (the 250-Hz-bandlimited local field potential, sampled at 1875 Hz) under leave-one-anesthetic-out evaluation across five mechanistically distinct anesthetics (isoflurane, dexmedetomidine, ketamine, propofol, and midazolam), comparing a spatially blind band-power decoder, covariance/Riemannian representations, and Riemannian domain adaptation, with all statistics at the session level and mouse-level cluster bootstrapping for the ketamine fold. The representation transfers: band-power ranks awake versus anesthetized at a session AUROC of at least 0.96 on every held-out drug, ketamine included (0.980, cluster confidence interval 0.821 to 1.000). The failure is confined to the threshold: across three representations the ketamine ranking is near-invariant while its balanced accuracy swings from chance to high, and a permutation test is significant for ranking (p = 0.0025) but not for fixed-threshold accuracy (p = 0.3795). Riemannian domain adaptation is net-negative. A causal, label-free threshold anchored to the subject's own pre-induction baseline fixes ketamine (balanced accuracy 0.50 to 0.85) and dominates domain adaptation. Because the ketamine test sessions come from three mice that also contribute training drugs, this is within-subject cross-drug transfer; we do not claim population-level transfer across subjects. In cross-drug state decoding the actionable failure is calibration, not representation.
